Rapid Identification of Nonlinear Material Parameters of Foams Based on Neural Network

Abstract

Closed cell polymeric foams, for example EVA foams, are widely used in sport and medical equipments such as helmets, seating, footwear and therapeutic plug/insoles. The mechanical behavior of foams is highly nonlinear, and the materials are often under complex loading conditions in service over different spectrums of strain. Foam testing and determination of the nonlinear foam constitutive parameters are important to predict materials performances in service and to aid product design and developments. One promising approach is to use the indentation method combined with inverse finite element (FE) programs. This approach has been extensively studied and several programs such as interactive, parametric and artificial neural networks (ANN) have found successful applications for different conditions. ANN represents a simple and quick method without repeated running of FE program, this could provide a significant advantage. In this study, an ANN based inverse FE program has been developed and used to predict the nonlinear material properties of EVA foams. A 2-D parametric FE model was developed and validated against experimental data. Systematic data from FE simulations was used to train and validate the ANN model. The accuracy and validity of the ANN method were assessed based on both blind tests and experimental data. Results showed that the proposed artificial neural network model is robust and efficient in predicating the nonlinear parameters of foam materials. Finally, potential applications of the method and future improvement are discussed.